Tourist Attraction Satisfaction Factors from Online Reviews. A Case Study of Tourist Attractions in Thailand
Abstract
In order to survive and gain competitive advantages in the post COVID-19 pandemic, tourism destinations should plan their business strategy by focusing on customer expectation. A number of research have studied tourist satisfaction, particularly, hotels and transports, however there is limited investigation with tourist attractions which have different prominence from other tourism service providers. The purpose of this study was to identify the tourists’ satisfaction components for tourist attractions by adopting an opinion mining technique and using the zero-shot text classification method. The total of 40,000 online tourists’ reviews from 40 tourist attractions in Thailand, that were posted up thought TripAdivisor.com between 2010 and 2021, were analyzed. The research findings reveal six components of tourist attraction satisfaction (TATSAT) model that includes 1) ambiance, 2) hospitality, 3) price, 4) accessibility, 5) cleanliness, and 6) security. All attributes of TATSAT model are generated from tourists’ point of view and was analyzed by the focus group discussion with five tourism experts from both academics and practitioners. This model expands the idea of HOLSAT and SERVQUAL by focusing on the tourist attraction business sector. The results can serve academics and practitioners in the research and improvement of tourist satisfaction to maximize competitive advantages for tourist attraction sector in the future.
References
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[29] Pizam, A., Neumann, Y. and Reichel, A. 1978. Dimentions of tourist satisfaction with a destination area. Annals of tourism Research 5(3): 314-322. DOI: https://doi.org/10.1016/0160-7383(78)90115-9
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[39] Verma, S. and Yadav, N. 2021. Past, present, and future of electronic word of mouth (EWOM). Journal of Interactive Marketing, 53: 111-128. DOI: https://doi.org/10.1016/j.intmar.2020.07.001
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[2] Alegre, J. and Garau, J. 2010. Tourist satisfaction and dissatisfaction. Annals of tourism research, 37(1): 52-73. DOI: 10.1016/j.annals.2009.07.001
[3] Baggio, R. and Caporarello, L. 2005. Decision support systems in a tourism destination: literature survey and model building. In U: Proceedings itAIS-2nd Conference of the Italian chapter of AIS (Association for Information Systems). Verona, Italy.
[4] Bernini, C. and Cagnone, S. 2014. Analysing tourist satisfaction at a mature and multi-product destination. Current Issues in Tourism, 17(1): 1-20. DOI: 10.1080/13683500.2012.702737
[5] Capriello, A., Mason, P.R., Davis, B. and Crotts, J. C. 2013. Farm tourism experiences in travel reviews: A cross-comparison of three alternative methods for data analysis. Journal of Business Research, 66(6): 778-785. DOI: 10.1016/j.jbusres.2011.09.018
[6] Ceylan, C. and Ozcelik, A. B. 2016. A circular approach to SERVQUAL and HOLSAT: An implementation suggestion. Journal of Hotel & Business Management, 5(1): 1-10. DOI: 10.4172/2169-0286.1000125
[7] Chenini, A., and Touaiti, M.2018. Building destination loyalty using tourist satisfaction and destination image: A holistic conceptual framework. Journal of Tourism, Heritage & Services Marketing, 4(2): 37-43. DOI:10.5281/zenodo.1490491
[8] Cherapanukorn, V. and Charoenkwan, P. 2017. Word cloud of online hotel reviews in Chiang Mai for customer satisfaction analysis. In 2017 International Conference on Digital Arts, Media and Technology (ICDAMT): 146-151. IEEE. DOI: 10.1109/ICDAMT.2017.7904952
[9] Crouch, G. I., and Ritchie, B.JR. 1999. Tourism, competitiveness, and societal prosperity. Journal of business research, 44(3): 137-152. DOI: https://doi.org/10.1016/S0148-2963(97)00196-3
[10] Del Chiaro, R., Bagdanov, A. D. and Del Bimbo, A. 2019. Webly-supervised zero-shot learning for artwork instance recognition. Pattern Recognition Letters, 128: 420-426. DOI:10.1016/j.patrec.2019.09.027
[11] Della Corte, V., Sciarelli, M., Cascella, C. and Del Gaudio, G. 2015. Customer satisfaction in tourist destination: The case of tourism offers in the city of Naples. Journal of Investment and Management, 4(1-1): 39-50. DOI: 10.11648/j.jim.s.2015040101.16
[12] Emel, G.G., Taşkin, Ç. and Akat, Ö. 2007. Profiling a domestic tourism market by means of association rule mining. Anatolia, 18(2): 334-342. DOI:10.1080/13032917.2007.9687209
[13] Eusébio, C. and Vieira, A.L. 2013. Destination attributes' evaluation, satisfaction and behavioural intentions: A structural modelling approach. International Journal of Tourism Research, 15(1): 66-80. DOI:10.1002/jtr.877
[14] Hu, Wei, and Wall, G.2005. Environmental management, environmental image and the competitive tourist attraction. Journal of sustainable tourism, 13(6): 617-635. DOI: 10.1080/09669580508668584
[15] Kang, N. and Yu, Q. 2011. Corpus-based Stylistic Analysis of Tourism English. Journal of Language Teaching & Research, 2(1): 129-136. DOI: 10.4304/jltr.2.1.129-136
[16] Khan, Khairullah, Baharum Baharudin, Aurnagzeb Khan, and Ashraf Ullah. 2014. “Mining opinion components from unstructured reviews: A review. Journal of King Saud University-Computer and Information Sciences 26(3): 258-275. DOI: https://doi.org/10.1016/j.jksuci.2014.03.009
[17] Khan, S. S., Khan, M., Ran, Q. and Naseem, R. 2018. Challenges in opinion mining, comprehensive review. A Science and Technology Journal, 33(11): 123-135. Available at: https://www.researchgate.net/publication/328980053
[18] Kozak, M. and Rimmington, M. 2000. Tourist satisfaction with Mallorca, Spain, as an off-season holiday destination. Journal of travel research, 38(3): 260-269. DOI: 10.1177/004728750003800308
[19] Lam, P. YW. 2007. A corpus-driven lexico-grammatical analysis of English tourism industry texts and the study of its pedagogic implications in English for Specific Purposes. In Corpora in the foreign language classroom: 71-89. Brill Rodopi. DOI: https://doi.org/10.1163/9789401203906_006
[20] Lee, C.C., and Hu, C. 2005. Analyzing Hotel customers' E-complaints from an internet complaint forum. Journal of Travel & Tourism Marketing 17(2-3): 167-181. DOI: 10.1300/J073v17n02_13
[21] Lew, A.A. et al. 2020. Visions of travel and tourism after the global COVID-19 transformation of 2020. Tourism Geographies 22(3): 455-466. DOI:10.1080/14616688.2020.1770326
[22] Li, H., Ye, Q. and Law, R. 2013. Determinants of customer satisfaction in the hotel industry: An application of online review analysis. Asia Pacific Journal of Tourism Research 18(7): 784-802. DOI:10.1080/10941665.2012.708351
[23] Lindquist, H. 2018. Corpus linguistics and the description of English. Edinburgh University Press.
[24] Liu, X., Mehraliyev, F., Liu, C. and Schuckert, M. 2020. The roles of social media in tourists’ choices of travel components. Tourist Studies, 20(1): 27-48. DOI: 10.1177/1468797619873107
[25] Marin, J.A. and Jaume Garau Taberner. 2008. Satisfaction and dissatisfaction with destination attributes: Influence on overall satisfaction and the intention to return. Retrieved December 18: 2011. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.612.6418&rep=rep1&type=pdf
[26] McDowall, S. 2010. International tourist satisfaction and destination loyalty: Bangkok, Thailand. Asia Pacific Journal of Tourism Research 15(1): 21-42. DOI: 10.1080/10941660903510040
[27] Parasuraman, A., Zeithaml, V.A. and Berry, L.L. 1985. A conceptual model of service quality and its implications for future research. Journal of marketing, 49(4): 41-50. DOI: 10.2307/1251430
[28] Pearce, P.1998. Marketing and management trends in tourist attractions. Asia Pacific Journal of Tourism Research 3(1): 1-8. DOI: https://doi.org/10.1080/10941669908722002
[29] Pizam, A., Neumann, Y. and Reichel, A. 1978. Dimentions of tourist satisfaction with a destination area. Annals of tourism Research 5(3): 314-322. DOI: https://doi.org/10.1016/0160-7383(78)90115-9
[30] Puangyaem, P. 2013. A Corpus Based Study of the Use of Adjectives in Tourist Websites. Language Institute, Thammasat University.
[31] Ritchie, J.R., Goeldner, C. R. and McIntosh, R. W. 2003. Tourism: principles, practices, philosophies. John Wiley & Son (New Jersey).
[32] Sahadev, S. and Islam, N. 2004. Exploring the determinants of e-commerce usage in the hotel industry in Thailand: An empirical study. Information Technology & Tourism, 7(3-4): 171-180. DOI:10.3727/109830505774297201
[33] Sappadla, P.V., Nam, J., Mencía, E.L. and Fürnkranz, J. 2016. Using semantic similarity for multi-label zero-shot classification of text documents. In ESANN 2016 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium, April, 27-29, 2016.
[34] Schmitt, N. and Schmitt, D. 2014. A reassessment of frequency and vocabulary size in L2 vocabulary teaching. Language Teaching, 47(4): 484-503. DOI: https://doi.org/10.1017/S0261444812000018
[35] Schuckert, M., Liu, X. and Law, R. 2015. Hospitality and tourism online reviews: Recent trends and future directions. Journal of Travel & Tourism Marketing, 32(5): 608-621. DOI:10.1080/10548408.2014.933154
[36] Shahrivar, R.B. 2012. Factors that influence tourist satisfaction. Journal of Travel and Tourism Research; Special Issue Destination Management-2012 (Online) 12(1): 61. Available at: https://www.researchgate.net/publication/284969853_Factors_that_influence_tourist_satisfaction
[37] Swarbrooke, J. and Page, S. J. 2012. Development and management of visitor attractions. Routledge.
[38] Tribe, J. and Snaith, T.1998. From SERVQUAL to HOLSAT: holiday satisfaction in Varadero, Cuba. Tourism management, 19(1): 25-34. DOI: https://doi.org/10.1016/S0261-5177(97)00094-0
[39] Verma, S. and Yadav, N. 2021. Past, present, and future of electronic word of mouth (EWOM). Journal of Interactive Marketing, 53: 111-128. DOI: https://doi.org/10.1016/j.intmar.2020.07.001
[40] Ye, M. and Guo, Y. 2017. Zero-shot classification with discriminative semantic representation learning.In Proceedings of the IEEE conference on computer vision and pattern recognition: 7140-7148. IEEE. DOI:10.1109/CVPR.2017.542
[41] Ye, Z. et al. 2020. Zero-shot text classification via reinforced self-training. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: 3014-3024. IEEE. DOI: 10.18653/v1/2020.acl-main.272
[42] Zhang, Z. et al. 2011. Sentiment classification of Internet restaurant reviews written in Cantonese. Expert Systems with Applications, 38(6): 7674-7682. DOI: 10.1016/j.eswa.2010.12.147
[43] UNWTO, 2019. International Tourism Highlights: 2019 Edition. Available at: https://www.e-unwto.org/doi/pdf/10.18111/9789284421152
[44] UNWTO, 2021. UNWTO Tourism Recovery Tracker. Available at: https://www.unwto.org/unwto-tourism-recovery-tracker
Published
2022-03-31
How to Cite
CHERAPANUKORN, Vimolboon; SUGUNNASIL, Prompong.
Tourist Attraction Satisfaction Factors from Online Reviews. A Case Study of Tourist Attractions in Thailand.
Journal of Environmental Management and Tourism, [S.l.], v. 13, n. 2, p. 379-390, mar. 2022.
ISSN 2068-7729.
Available at: <https://journals.aserspublishing.eu/jemt/article/view/6893>. Date accessed: 26 apr. 2024.
doi: https://doi.org/10.14505/jemt.v13.2(58).08.
Section
Article
Copyright© 2023 The Author(s). Published by ASERS Publishing 2023. This is an open access article distributed under the terms of CC-BY 4.0 license.